Optimizing Hospital-wide Patient Scheduling - Early Classification of Diagnosis-related Groups Through Machine Learning (Paperback, 2014 ed.)

Diagnosis-related groups (DRGs) are used in hospitals for the reimbursement of inpatient services. The assignment of a patient to a DRG can be distinguished into billing- and operations-driven DRG classification. The topic of this monograph is operations-drivenDRG classification, in which DRGs of inpatients are employed to improve contribution margin-based patient scheduling decisions. In the first part, attribute selection and classification techniques are evaluated in order to increase early DRG classification accuracy. Employing mathematical programming, the hospital-wide flow of elective patients is modelled taking into account DRGs, clinical pathways and scarce hospital resources. The results of the early DRG classification part reveal that a small set of attributes is sufficient in order to substantially improve DRG classification accuracy as compared to the current approach of many hospitals. Moreover, the results of the patient scheduling part reveal that the contribution margin can be increased as compared to current practice."

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Lecture Notes in Economics and Mathematical Systems, 674
Release date:	June 2015
First published:	2014
Authors:	Daniel Gartner
Dimensions:	235 x 155 x 7mm (L x W x T)
Format:	Paperback
Pages:	119
Edition:	2014 ed.
ISBN-13:	978-3-319-04065-3
Categories:	Books > Business & Economics > Business & management > Management & management techniques > Operational research Books > Medicine > General issues > Health systems & services > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-319-04065-0
Barcode:	9783319040653

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